Research on a High-Efficiency Goat Individual Recognition Method Based on Machine Vision

被引:1
作者
Xue, Yi [1 ]
Wang, Weiwei [1 ]
Fang, Mei [2 ]
Guo, Zhiming [1 ]
Ning, Keke [1 ]
Wang, Kui [1 ]
机构
[1] Anhui Agr Univ, Res Ctr Intelligent Farming Equipment, Sch Engn, Hefei 230036, Peoples R China
[2] Anhui Sci & Technol Univ, Coll Mech Engn, Chuzhou 233100, Peoples R China
来源
ANIMALS | 2024年 / 14卷 / 23期
关键词
precision farming; machine vision; multi-source fusion; identity recognition; multi-view appearance; decision fusion; FACE RECOGNITION; IDENTIFICATION;
D O I
10.3390/ani14233509
中图分类号
S8 [畜牧、 动物医学、狩猎、蚕、蜂];
学科分类号
0905 ;
摘要
Accurate identification of individual goat identity is necessary for precision farming. Previous studies have primarily focused on using front face images for goat identification, leaving the potential of other appearances and multi-source appearance fusion unexplored. In this study, we used a self-developed multi-view appearance image acquisition platform to capture five different appearances (left face, right face, front face, back body, and side body) from 54 Wanlin white goats. The recognition ability of different goat appearance images and its multi-source appearance fusion for its identity recognition was then systematically examined based on the four basic network models, namely, MobileNetV3, MobileViT, ResNet18, and VGG16, and the best combination of goat appearance and network was screened. When only one kind of goat appearance image was used, the combination of side body image and MobileViT was the best, with an accuracy of 99.63%; under identity recognition based on multi-source image appearance fusion, all recognition models after outlook fusion of two viewpoints generally outperformed single viewpoint appearance identity recognition models in recognizing the identity of individual goats; when three or more kinds of goat appearance images were utilized for fusion, any of the four models were capable of identifying the identity of an individual goat with 100% accuracy. Based on these results, a goat individual identity recognition strategy was proposed that balances accuracy, computation, and time, providing new ideas for goat individual identity recognition in complex farming contexts.
引用
收藏
页数:23
相关论文
共 28 条
[1]  
Bello R.-W., 2023, World Sci. News, V181, P68
[2]   Real-time goat face recognition using convolutional neural network [J].
Billah, Masum ;
Wang, Xihong ;
Yu, Jiantao ;
Jiang, Yu .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2022, 194
[3]  
Caja G., 2004, ICAR Technical Series, P21
[4]  
Corkery GP, 2007, T ASABE, V50, P313, DOI 10.13031/2013.22395
[5]   Biometric identification systems [J].
de Luis-García, R ;
Alberola-López, C ;
Aghzout, O ;
Ruiz-Alzola, J .
SIGNAL PROCESSING, 2003, 83 (12) :2539-2557
[6]   Facial Recognition of Cattle Based on SK-ResNet [J].
Gong, He ;
Pan, Haohong ;
Chen, Lin ;
Hu, TianLi ;
Li, Shijun ;
Sun, Yu ;
Mu, Ye ;
Guo, Ying .
SCIENTIFIC PROGRAMMING, 2022, 2022
[7]   Towards on-farm pig face recognition using convolutional neural networks [J].
Hansen, Mark E. ;
Smith, Melvyn L. ;
Smith, Lyndon N. ;
Salter, Michael G. ;
Baxter, Emma M. ;
Farish, Marianne ;
Grieve, Bruce .
COMPUTERS IN INDUSTRY, 2018, 98 :145-152
[8]   Deep Residual Learning for Image Recognition [J].
He, Kaiming ;
Zhang, Xiangyu ;
Ren, Shaoqing ;
Sun, Jian .
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, :770-778
[9]   Biometric identification of sheep via a machine-vision system [J].
Hitelman, Almog ;
Edan, Yael ;
Godo, Assaf ;
Berenstein, Ron ;
Lepar, Joseph ;
Halachmi, Ilan .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2022, 194
[10]  
Hore Alain, 2010, Proceedings of the 2010 20th International Conference on Pattern Recognition (ICPR 2010), P2366, DOI 10.1109/ICPR.2010.579